Bias against overweight job applicants in a simulated employment interview.
Abstract
This study assessed whether moderately obese individuals, especially women, would be discriminated against in a mock employment interview. Potential confounding factors were controlled by having 320 Ss rate videotapes of a job interview that used the same professional actors appearing as normal weight or made up to appear overweight by the use of theatrical prostheses. Results suggested that bias against hiring overweight job applicants does exist, especially for female applicants. Bias was most pronounced when applicants were rated by Ss who were satisfied with their bodies and for whom perceptions of their bodies were central to self-concept. The decision not to hire an obese applicant was, however, only partially mediated by personality attributions. Implications and limitations of these results are discussed.
Cites (1)
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Cited by (16)
- Bias, Discrimination, and ObesityPuhl, Rebecca, Brownell, Kelly D. · 2001 · Obesity Research
- A meta-analysis of gender stereotypes and bias in experimental simulations of employment decision making.Koch, Amanda J., D'Mello, Susan D., Sackett, Paul R. · 2015 · Journal of Applied Psychology
- Experimental research on labor market discriminationNeumark, David · 2018 · Journal of Economic Literature
- Is Fat a Feminist Issue? Exploring the Gendered Nature of Weight BiasFikkan, Janna L., Rothblum, Esther D. · 2012 · Sex Roles
- Weighing the care: physicians' reactions to the size of a patientHebl, MR, Xu, J · 2001 · International Journal of Obesity
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bias against overweight job applicants in a simulated employment interview. https://app.cochat.ai/papers/10.1037//0021-9010.79.6.909
- The paper
- Pingitore, R., Dugoni, B. L., Tindale, R. S., & Spring, B. J. (1994). Bias against overweight job applicants in a simulated employment interview. Journal of Applied Psychology. https://doi.org/10.1037//0021-9010.79.6.909
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.